The phishing is a technique used by cyber-criminals to impersonate legitimate websites in order to obtain personal information. This paper presents a novel lightweight phishing detection approach completely based on the URL (uniform resource locator). The mentioned system produces a very satisfying recognition rate which is 95.80%. This system, is an SVM (support vector machine) tested on a 2000 records data-set consisting of 1000 legitimate and 1000 phishing URLs records. In the literature, several works tackled the phishing attack. However those systems are not optimal to smartphones and other embed devices because of their complex computing and their high battery usage. The proposed system uses only six URL features to perform the recognition. The mentioned features are the URL size, the number of hyphens, the number of dots, the number of numeric characters plus a discrete variable that correspond to the presence of an IP address in the URL and finally the similarity index. Proven by the results of this study the similarity index, the feature we introduce for the first time as input to the phishing detection systems improves the overall recognition rate by 21.8%.

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In this article, the authors propose a new innovative method based on blockchain technology providing an analysis of Android applications in a decentralized, flexible, and reliable way. The proposed approach improves the typical operation of the blockchain technology that considers invalid (or “fraudulent”) any outcome different from other results found by the majority of network nodes. However, ignoring any result different from the majority without starting additional verification can cause losses in terms of data, time, computing power, or even system reliability and the integrity of its data. The purpose of the presented approach is to confirm or deny the legitimacy of any outcome different from the majority. This new concept will facilitate the detection of polymorphic programs by allowing nodes to adopt specific environments at any time to reduce the rejection of results deemed, wrongly, to be fraudulent. A proof of concept has been designed and implemented showing the feasibility of the proposed approach with a real case study.